Use of machine learning to transform complex standardized nursing care plan data into meaningful research variables: a palliative care exemplar.
J Am Med Inform Assoc
; 28(12): 2695-2701, 2021 11 25.
Article
em En
| MEDLINE
| ID: mdl-34569603
ABSTRACT
The aim of this article was to describe a novel methodology for transforming complex nursing care plan data into meaningful variables to assess the impact of nursing care. We extracted standardized care plan data for older adults from the electronic health records of 4 hospitals. We created a palliative care framework with 8 categories. A subset of the data was manually classified under the framework, which was then used to train random forest machine learning algorithms that performed automated classification. Two expert raters achieved a 78% agreement rate. Random forest classifiers trained using the expert consensus achieved accuracy (agreement with consensus) between 77% and 89%. The best classifier was utilized for the automated classification of the remaining data. Utilizing machine learning reduces the cost of transforming raw data into representative constructs that can be used in research and practice to understand the essence of nursing specialty care, such as palliative care.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Cuidados Paliativos
/
Aprendizado de Máquina
Tipo de estudo:
Guideline
Limite:
Aged
/
Humans
Idioma:
En
Revista:
J Am Med Inform Assoc
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos